# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for MegatronBert."""

from .. import BertTokenizer

__all__ = ['MegatronBertTokenizer']


class MegatronBertTokenizer(BertTokenizer):
    """
        Constructs a MegatronBert tokenizer. It uses a basic tokenizer to do punctuation
        splitting, lower casing and so on, and follows a WordPiece tokenizer to
        tokenize as subwords.

        Args:
            vocab_file (str):
                The vocabulary file path (ends with '.txt') required to instantiate
                a `WordpieceTokenizer`.
            do_lower_case (bool):
                Whether or not to lowercase the input when tokenizing.
                Defaults to`True`.
            unk_token (str):
                A special token representing the *unknown (out-of-vocabulary)* token.
                An unknown token is set to be `unk_token` inorder to be converted to an ID.
                Defaults to "[UNK]".
            sep_token (str):
                A special token separating two different sentences in the same input.
                Defaults to "[SEP]".
            pad_token (str):
                A special token used to make arrays of tokens the same size for batching purposes.
                Defaults to "[PAD]".
            cls_token (str):
                A special token used for sequence classification. It is the last token
                of the sequence when built with special tokens. Defaults to "[CLS]".
            mask_token (str):
                A special token representing a masked token. This is the token used
                in the masked language modeling task which the model tries to predict the original unmasked ones.
                Defaults to "[MASK]".

        Examples:
            .. code-block::

                from paddlenlp.transformers import MegatronBertTokenizer
                tokenizer = MegatronBertTokenizer.from_pretrained('MegatronBert-uncased')
                inputs = tokenizer('He was a puppeteer')
                print(inputs)

                '''
                {'input_ids': [101, 2002, 2001, 1037, 13997, 11510, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0]}
                '''

        """
    resource_files_names = {"vocab_file": "vocab.txt"}  # for save_pretrained
    pretrained_resource_files_map = {
        "vocab_file": {
            "megatronbert-uncased":
            "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-uncased-vocab.txt",
            "megatronbert-cased":
            "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-cased-vocab.txt",
        }
    }
    pretrained_init_configuration = {
        "megatronbert-uncased": {
            "do_lower_case": True
        },
        "megatronbert-cased": {
            "do_lower_case": False
        }
    }

    def __init__(self,
                 vocab_file,
                 do_lower_case=True,
                 unk_token="[UNK]",
                 sep_token="[SEP]",
                 pad_token="[PAD]",
                 cls_token="[CLS]",
                 mask_token="[MASK]",
                 **kwargs):
        super(MegatronBertTokenizer, self).__init__(
            vocab_file,
            do_lower_case=do_lower_case,
            unk_token=unk_token,
            sep_token=sep_token,
            pad_token=pad_token,
            cls_token=cls_token,
            mask_token=mask_token)
